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Clinical and biological relevance of genomic heterogeneity in chronic lymphocytic leukemia.

Friedman DR, Lucas JE, Weinberg JB - PLoS ONE (2013)

Bottom Line: We used unsupervised approaches to divide the data into subgroups, evaluated the biological pathways and genetic aberrations that were associated with the subgroups, and compared prognostic and clinical outcome data between the subgroups.We identified seven genomically-defined CLL subgroups that have distinct biological properties, are associated with specific chromosomal deletions and amplifications, and have marked differences in molecular prognostic markers and clinical outcomes.Our results indicate that investigations focusing on small numbers of patient samples likely provide a biased outlook on CLL biology.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicine, Duke University, Durham, North Carolina, USA. daphne.friedman@duke.edu

ABSTRACT

Background: Chronic lymphocytic leukemia (CLL) is typically regarded as an indolent B-cell malignancy. However, there is wide variability with regards to need for therapy, time to progressive disease, and treatment response. This clinical variability is due, in part, to biological heterogeneity between individual patients' leukemias. While much has been learned about this biological variation using genomic approaches, it is unclear whether such efforts have sufficiently evaluated biological and clinical heterogeneity in CLL.

Methods: To study the extent of genomic variability in CLL and the biological and clinical attributes of genomic classification in CLL, we evaluated 893 unique CLL samples from fifteen publicly available gene expression profiling datasets. We used unsupervised approaches to divide the data into subgroups, evaluated the biological pathways and genetic aberrations that were associated with the subgroups, and compared prognostic and clinical outcome data between the subgroups.

Results: Using an unsupervised approach, we determined that approximately 600 CLL samples are needed to define the spectrum of diversity in CLL genomic expression. We identified seven genomically-defined CLL subgroups that have distinct biological properties, are associated with specific chromosomal deletions and amplifications, and have marked differences in molecular prognostic markers and clinical outcomes.

Conclusions: Our results indicate that investigations focusing on small numbers of patient samples likely provide a biased outlook on CLL biology. These findings may have important implications in identifying patients who should be treated with specific targeted therapies, which could have efficacy against CLL cells that rely on specific biological pathways.

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Related in: MedlinePlus

Results of Consensus Clustering to define the smallest number of subgroups that define genomic variation within the entire combined dataset.A) A heatmap of the consensus matrix, displaying samples with high consensus (blue) are grouped together, compared to those with low consensus (white). B) The delta area plot shows a negligible increase in area under the Consensus cumulative distribution function in more than seven subgroups. Thus, dividing the data into more subgroups does not improve sample classification.
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pone-0057356-g002: Results of Consensus Clustering to define the smallest number of subgroups that define genomic variation within the entire combined dataset.A) A heatmap of the consensus matrix, displaying samples with high consensus (blue) are grouped together, compared to those with low consensus (white). B) The delta area plot shows a negligible increase in area under the Consensus cumulative distribution function in more than seven subgroups. Thus, dividing the data into more subgroups does not improve sample classification.

Mentions: To assess the extent of genomic heterogeneity in CLL, we evaluated the combined dataset with unsupervised hierarchical clustering using the Consensus Clustering algorithm. As seen in Figure 2, this approach identifies seven subgroups that group together based on consensus. Further dividing of the entire dataset into a larger number of subgroups has minimal improvement in classification, as demonstrated by the minimal change in the Consensus cumulative distribution function (CDF) beyond seven subgroups.


Clinical and biological relevance of genomic heterogeneity in chronic lymphocytic leukemia.

Friedman DR, Lucas JE, Weinberg JB - PLoS ONE (2013)

Results of Consensus Clustering to define the smallest number of subgroups that define genomic variation within the entire combined dataset.A) A heatmap of the consensus matrix, displaying samples with high consensus (blue) are grouped together, compared to those with low consensus (white). B) The delta area plot shows a negligible increase in area under the Consensus cumulative distribution function in more than seven subgroups. Thus, dividing the data into more subgroups does not improve sample classification.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3585365&req=5

pone-0057356-g002: Results of Consensus Clustering to define the smallest number of subgroups that define genomic variation within the entire combined dataset.A) A heatmap of the consensus matrix, displaying samples with high consensus (blue) are grouped together, compared to those with low consensus (white). B) The delta area plot shows a negligible increase in area under the Consensus cumulative distribution function in more than seven subgroups. Thus, dividing the data into more subgroups does not improve sample classification.
Mentions: To assess the extent of genomic heterogeneity in CLL, we evaluated the combined dataset with unsupervised hierarchical clustering using the Consensus Clustering algorithm. As seen in Figure 2, this approach identifies seven subgroups that group together based on consensus. Further dividing of the entire dataset into a larger number of subgroups has minimal improvement in classification, as demonstrated by the minimal change in the Consensus cumulative distribution function (CDF) beyond seven subgroups.

Bottom Line: We used unsupervised approaches to divide the data into subgroups, evaluated the biological pathways and genetic aberrations that were associated with the subgroups, and compared prognostic and clinical outcome data between the subgroups.We identified seven genomically-defined CLL subgroups that have distinct biological properties, are associated with specific chromosomal deletions and amplifications, and have marked differences in molecular prognostic markers and clinical outcomes.Our results indicate that investigations focusing on small numbers of patient samples likely provide a biased outlook on CLL biology.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicine, Duke University, Durham, North Carolina, USA. daphne.friedman@duke.edu

ABSTRACT

Background: Chronic lymphocytic leukemia (CLL) is typically regarded as an indolent B-cell malignancy. However, there is wide variability with regards to need for therapy, time to progressive disease, and treatment response. This clinical variability is due, in part, to biological heterogeneity between individual patients' leukemias. While much has been learned about this biological variation using genomic approaches, it is unclear whether such efforts have sufficiently evaluated biological and clinical heterogeneity in CLL.

Methods: To study the extent of genomic variability in CLL and the biological and clinical attributes of genomic classification in CLL, we evaluated 893 unique CLL samples from fifteen publicly available gene expression profiling datasets. We used unsupervised approaches to divide the data into subgroups, evaluated the biological pathways and genetic aberrations that were associated with the subgroups, and compared prognostic and clinical outcome data between the subgroups.

Results: Using an unsupervised approach, we determined that approximately 600 CLL samples are needed to define the spectrum of diversity in CLL genomic expression. We identified seven genomically-defined CLL subgroups that have distinct biological properties, are associated with specific chromosomal deletions and amplifications, and have marked differences in molecular prognostic markers and clinical outcomes.

Conclusions: Our results indicate that investigations focusing on small numbers of patient samples likely provide a biased outlook on CLL biology. These findings may have important implications in identifying patients who should be treated with specific targeted therapies, which could have efficacy against CLL cells that rely on specific biological pathways.

Show MeSH
Related in: MedlinePlus